Utilizing Quantum Annealing in Computed Tomography Image Reconstruction

One of the primary difficulties in computed tomography (CT) is reconstructing cross-sectional images from measured projections of a physical object. There exist several classical methods for this task of generating a digital representation of the object, including filtered backprojection or simultaneous algebraic reconstruction technique. Our research aims to explore the potential of quantum computing in the field of industrial X-ray transmission tomography. Specifically, this work focuses on the application of a method similar to that proposed by Nau et al. (2023) on real CT data to demonstrate the feasibility of quadratic-unconstrained-binary-optimization-based tomographic reconstruction. Starting with simulated phantoms, results with simulated annealing as well as real annealing hardware are shown, leading to the application on measured cone-beam CT data. The results demonstrate that tomographic reconstruction using quantum annealing is feasible for both simulated and real-world applications. Yet, current limitations—involving the maximum processable size and bit depth of voxel values of the images, both correlated with the number of densely connected qubits within the annealing hardware—imply the need of future research to further improve the results. This approach, despite its early stage, has the potential to enable more sophisticated reconstructions, providing an alternative to traditional classical methods.